The Experts below are selected from a list of 423 Experts worldwide ranked by ideXlab platform
Ulf Norinder - One of the best experts on this subject based on the ideXlab platform.
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Maximizing gain in high-throughput screening using Conformal prediction
Journal of Cheminformatics, 2018Co-Authors: Fredrik Svensson, Ulf Norinder, Avid M. Afzal, Andreas BenderAbstract:Iterative screening has emerged as a promising approach to increase the efficiency of screening campaigns compared to traditional high throughput approaches. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models, resulting in more efficient screening. One way to evaluate screening is to consider the cost of screening compared to the gain associated with finding an active compound. In this work, we introduce a Conformal Predictor coupled with a gain-cost function with the aim to maximise gain in iterative screening. Using this setup we were able to show that by evaluating the predictions on the training data, very accurate predictions on what settings will produce the highest gain on the test data can be made. We evaluate the approach on 12 bioactivity datasets from PubChem training the models using 20% of the data. Depending on the settings of the gain-cost function, the settings generating the maximum gain were accurately identified in 8–10 out of the 12 datasets. Broadly, our approach can predict what strategy generates the highest gain based on the results of the cost-gain evaluation: to screen the compounds predicted to be active, to screen all the remaining data, or not to screen any additional compounds. When the algorithm indicates that the predicted active compounds should be screened, our approach also indicates what confidence level to apply in order to maximize gain. Hence, our approach facilitates decision-making and allocation of the resources where they deliver the most value by indicating in advance the likely outcome of a screening campaign.
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improving screening efficiency through iterative screening using docking and Conformal prediction
Journal of Chemical Information and Modeling, 2017Co-Authors: Fredrik Svensson, Ulf Norinder, Andreas BenderAbstract:High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a Conformal Predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the acti...
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Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction
2017Co-Authors: Fredrik Svensson, Ulf Norinder, Andreas BenderAbstract:High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a Conformal Predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the active compounds could be identified, compared to 13.5% when screening the same number of compounds solely based on docking. The use of Conformal Predictors also gives a clear indication of the number of compounds to screen in the next iteration. These results indicate that iterative screening based on molecular docking and Conformal prediction can be an efficient way to find active compounds while screening only a small part of the compound collection
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The application of Conformal prediction to the drug discovery process
Annals of Mathematics and Artificial Intelligence, 2015Co-Authors: Martin Eklund, Scott Boyer, Ulf Norinder, Lars CarlssonAbstract:QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive Conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the Conformal Predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line Conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that Conformal predictions are highly useful in the drug discovery process.
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introducing Conformal prediction in predictive modeling for regulatory purposes a transparent and flexible alternative to applicability domain determination
Regulatory Toxicology and Pharmacology, 2015Co-Authors: Ulf Norinder, Scott Boyer, Martin Eklund, Lars CarlssonAbstract:Abstract Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure–activity data where focus is on predictions with pre-defined confidence levels. A Conformal Predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the Predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of Conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.
Martin Eklund - One of the best experts on this subject based on the ideXlab platform.
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The application of Conformal prediction to the drug discovery process
Annals of Mathematics and Artificial Intelligence, 2015Co-Authors: Martin Eklund, Scott Boyer, Ulf Norinder, Lars CarlssonAbstract:QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive Conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the Conformal Predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line Conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that Conformal predictions are highly useful in the drug discovery process.
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introducing Conformal prediction in predictive modeling for regulatory purposes a transparent and flexible alternative to applicability domain determination
Regulatory Toxicology and Pharmacology, 2015Co-Authors: Ulf Norinder, Scott Boyer, Martin Eklund, Lars CarlssonAbstract:Abstract Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure–activity data where focus is on predictions with pre-defined confidence levels. A Conformal Predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the Predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of Conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.
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introducing Conformal prediction in predictive modeling for regulatory purposes a transparent and flexible alternative to applicability domain determination
Regulatory Toxicology and Pharmacology, 2015Co-Authors: Ulf Norinder, Scott Boyer, Martin Eklund, Lars CarlssonAbstract:Abstract Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure–activity data where focus is on predictions with pre-defined confidence levels. A Conformal Predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the Predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of Conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.
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aggregated Conformal prediction
Artificial Intelligence Applications and Innovations, 2014Co-Authors: Lars Carlsson, Martin Eklund, Ulf NorinderAbstract:We present the aggregated Conformal Predictor (ACP), an extension to the traditional inductive Conformal prediction (ICP) where several inductive Conformal Predictors are applied on the same training set and their individual predictions are aggregated to form a single prediction on an example. The results from applying ACP on two pharmaceutical data sets (CDK5 and GNRHR) indicate that the ACP has advantages over traditional ICP. ACP reduces the variance of the prediction region estimates and improves efficiency. Still, it is more conservative in terms of validity than ICP, indicating that there is room for further improvement of efficiency without compromising validity.
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introducing Conformal prediction in predictive modeling a transparent and flexible alternative to applicability domain determination
Journal of Chemical Information and Modeling, 2014Co-Authors: Ulf Norinder, Scott Boyer, Lars Carlsson, Martin EklundAbstract:Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using Conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in Conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the Conformal Predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of Conformal prediction by applying it to 10 publicly available data sets.
Guang Li - One of the best experts on this subject based on the ideXlab platform.
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Conformal prediction based on k nearest neighbors for discrimination of ginsengs by a home made electronic nose
Sensors, 2017Co-Authors: Zhan Wang, Jiacheng Miao, You Wang, Guang LiAbstract:An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called Conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of Conformal prediction. In offline mode, the Conformal Predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that Conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.
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Conformal prediction based on k nearest neighbors for discrimination of ginsengs by a home made electronic nose
Sensors, 2017Co-Authors: Zhan Wang, Jiacheng Miao, You Wang, Guang LiAbstract:An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called Conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of Conformal prediction. In offline mode, the Conformal Predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that Conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.
Zhan Wang - One of the best experts on this subject based on the ideXlab platform.
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discrimination of different species of dendrobium with an electronic nose using aggregated Conformal Predictor
Sensors, 2019Co-Authors: You Wang, Zhan Wang, Junwei Diao, Xiyang Sun, Zhiyuan LuoAbstract:A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated Conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive Conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of Conformal Predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated Conformal prediction to classify the species of dendrobium with reliability and validity is promising.
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Conformal prediction based on k nearest neighbors for discrimination of ginsengs by a home made electronic nose
Sensors, 2017Co-Authors: Zhan Wang, Jiacheng Miao, You Wang, Guang LiAbstract:An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called Conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of Conformal prediction. In offline mode, the Conformal Predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that Conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.
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Conformal prediction based on k nearest neighbors for discrimination of ginsengs by a home made electronic nose
Sensors, 2017Co-Authors: Zhan Wang, Jiacheng Miao, You Wang, Guang LiAbstract:An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called Conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of Conformal prediction. In offline mode, the Conformal Predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that Conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.
Huazhen Wang - One of the best experts on this subject based on the ideXlab platform.
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reliable multi label learning via Conformal Predictor and random forest for syndrome differentiation of chronic fatigue in traditional chinese medicine
PLOS ONE, 2014Co-Authors: Huazhen Wang, Fan Yang, Xin Liu, Yanzhu HongAbstract:National Natural Science Fundation of China [61202144, 61203282, 61300138]; Natural Science Foundation of Fujian Province China [2012J01274, 2012J05125]; Research Grant Council of Huaqiao University [09BS515]
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Local Clustering Conformal Predictor for Imbalanced Data Classification
2013Co-Authors: Huazhen Wang, Zhigang Chen, Yewang Chen, Fan YangAbstract:The recently developed Conformal Predictor (CP) can provide calibrated confidence for prediction which is out of the traditional Predictors’ capacity. However, CP works for balanced data and fails in the case of imbalanced data. To handle this problem, Local Clustering Conformal Predictor (LCCP) which plugs a two-level partition into the framework of CP is proposed. In the first-level partition, the whole imbalanced training dataset is partitioned into some class-taxonomy data subsets. Secondly, the majority class examples proceed to be partitioned into some cluster-taxonomy data subsets by clustering method. To predict a new instance, LCCP selects the nearest cluster, incorporated with the minority class examples, to build a re-balanced training data. The designed LCCP model aims to not only provide valid confidence for prediction, but significantly improve the prediction efficiency as well. The experimental results show that LCCP model presents superiority than CP model for imbalanced data classification.
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Distance Metric Learning-Based Conformal Predictor
2012Co-Authors: Fan Yang, Zhigang Chen, Guifang Shao, Huazhen WangAbstract:In order to improve the computational efficiency of Conformal Predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a nonconformity measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm.
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Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
BMC Bioinformatics, 2009Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen CaiAbstract:Abstract Background Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results In this paper, we present a modified random forest classifier which is incorporated into the Conformal Predictor scheme. A Conformal Predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional Predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion The method of using RF outlier measure to design a nonconformity measure benefits the resultant Predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.